scholarly journals The Rapid Mood Screener: A Novel and Pragmatic Screener Tool for Bipolar I Disorder

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 167-168
Author(s):  
C. Brendan Montano ◽  
Mehul Patel ◽  
Rakesh Jain ◽  
Prakash S. Masand ◽  
Amanda Harrington ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Health Outcomes ◽  
Screening Tool ◽  
Clinical Interview ◽  
Clinical Practices ◽  
Predictive Values ◽  
Bipolar I Disorder ◽  
Manic Symptoms ◽  
Depressive Episodes

AbstractIntroductionApproximately 70% of patients with bipolar disorder (BPD) are initially misdiagnosed, resulting in significantly delayed diagnosis of 7–10 years on average. Misdiagnosis and diagnostic delay adversely affect health outcomes and lead to the use of inappropriate treatments. As depressive episodes and symptoms are the predominant symptom presentation in BPD, misdiagnosis as major depressive disorder (MDD) is common. Self-rated screening instruments for BPD exist but their length and reliance on past manic symptoms are barriers to implementation, especially in primary care settings where many of these patients initially present. We developed a brief, pragmatic bipolar I disorder (BPD-I) screening tool that not only screens for manic symptoms but also includes risk factors for BPD-I (eg, age of depression onset) to help clinicians reduce the misdiagnosis of BPD-I as MDD.MethodsExisting questionnaires and risk factors were identified through a targeted literature search; a multidisciplinary panel of experts participated in 2 modified Delphi panels to select concepts thought to differentiate BPD-I from MDD. Individuals with self-reported BPD-I or MDD participated in cognitive debriefing interviews (N=12) to test and refine item wording. A multisite, cross-sectional, observational study was conducted to evaluate the screening tool’s predictive validity. Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a draft 10-item screening tool and additional questionnaires/questions. Different combinations of item sets with various item permutations (eg, number of depressive episodes, age of onset) were simultaneously tested. The final combination of items and thresholds was selected based on multiple considerations including clinical validity, optimization of sensitivity and specificity, and pragmatism.ResultsA total of 160 clinical interviews were conducted; 139 patients had clinical interview-confirmed BPD-I (n=67) or MDD (n=72). The screening tool was reduced from 10 to 6 items based on item-level analysis. When 4 items or more were endorsed (yes) in this analysis sample, the sensitivity of this tool for identifying patients with BPD-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.ConclusionThis new 6-item BPD-I screening tool serves to differentiate BPD-I from MDD in patients with depressive symptoms. Use of this tool can provide real-world guidance to primary care practitioners on whether more comprehensive assessment for BPD-I is warranted. Use of a brief and valid tool provides an opportunity to reduce misdiagnosis, improve treatment selection, and enhance health outcomes in busy clinical practices.FundingAbbVie Inc.

scholarly journals Healthcare Provider Perspectives on Bipolar I Disorder Screening and the Rapid Mood Screener (RMS), a Pragmatic, New Tool

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 181-183
Author(s):  
Michael E. Thase ◽  
Stephen M. Stahl ◽  
Roger S. McIntyre ◽  
Tina Matthews-Hayes ◽  
Mehul Patel ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Nurse Practitioners ◽  
Primary Care Physicians ◽  
Screening Tool ◽  
Positive Impact ◽  
Screening Tools ◽  
Predictive Values ◽  
Bipolar I Disorder ◽  
Provider Perspectives ◽  
Patient Reported

AbstractIntroductionAlthough mania is the hallmark symptom of bipolar I disorder (BD-I), most patients initially present for treatment with depressive symptoms. Misdiagnosis of BD-I as major depressive disorder (MDD) is common, potentially resulting in poor outcomes and inappropriate antidepressant monotherapy treatment. Screening patients with depressive symptoms is a practical strategy to help healthcare providers (HCPs) identify when additional assessment for BD-I is warranted. The new 6-item Rapid Mood Screener (RMS) is a pragmatic patient-reported BD-I screening tool that relies on easily understood terminology to screen for manic symptoms and other BD-I features in <2 minutes. The RMS was validated in an observational study in patients with clinically confirmed BD-I (n=67) or MDD (n=72). When 4 or more items were endorsed (“yes”), the sensitivity of the RMS for identifying patients with BP-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. To more thoroughly understand screening tool use among HCPs, a 10-minute survey was conducted.MethodsA nationwide sample of HCPs (N=200) was selected using multiple HCP panels; HCPs were asked to describe their opinions/current use of screening tools, assess the RMS, and evaluate the RMS versus the widely recognized Mood Disorder Questionnaire (MDQ). Results were reported by grouped specialties (primary care physicians, general nurse practitioners [NPs]/physician assistants [PAs], psychiatrists, and psychiatric NPs/PAs). Included HCPs were in practice <30 years, spent at least 75% of their time in clinical practice, saw at least 10 patients with depression per month, and diagnosed MDD or BD in at least 1 patient per month. Findings were reported using descriptive statistics; statistical significance was reported at the 95% confidence interval.ResultsAmong HCPs, 82% used a tool to screen for MDD, while 32% used a tool for BD. Screening tool attributes considered to be of the greatest value included sensitivity (68%), easy to answer questions (66%), specificity (65%), confidence in results (64%), and practicality (62%). Of HCPs familiar with screening tools, 70% thought the RMS was at least somewhat better than other screening tools. Most HCPs were aware of the MDQ (85%), but only 29% reported current use. Most HCPs (81%) preferred the RMS to the MDQ, and the RMS significantly outperformed the MDQ across valued attributes; 76% reported that they were likely to use the RMS to screen new patients with depressive symptoms. A total of 84% said the RMS would have a positive impact on their practice, with 46% saying they would screen more patients for bipolar disorder.DiscussionThe RMS was viewed positively by HCPs who participated in a brief survey. A large percentage of respondents preferred the RMS over the MDQ and indicated that they would use it in their practice. Collectively, responses indicated that the RMS is likely to have a positive impact on screening behavior.FundingAbbVie Inc.

scholarly journals Risk factors for new depressive episodes in primary health care: an international prospective 12-month follow-up study

2002 ◽  
Vol 32 (4) ◽  
pp. 595-607 ◽  
Author(s):  
K. BARKOW ◽  
W. MAIER ◽  
T. B. ÜSTÜN ◽  
M. GÄNSICKE ◽  
H.-U. WITTCHEN ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Depressive Episode ◽  
Icd 10 ◽  
Depressive Episodes ◽  
Psychiatric Problems

Background. Studies that examined community samples have reported several risk factors for the development of depressive episodes. The few studies that have been performed on primary care samples were mostly cross-sectional. Most samples had originated from highly developed industrial countries. This is the first study that prospectively investigates the risk factors of depressive episodes in an international primary care sample.Methods. A stratified primary care sample of initially non-depressed subjects (N = 2445) from 15 centres from all over the world was examined for the presence or absence of a depressive episode (ICD-10) at the 12 month follow-up assessment. The initial measures addressed sociodemographic variables, psychological/psychiatric problems and social disability. Logistic regression analysis was carried out to determine their relationship with the development of new depressive episodes.Results. At the 12-month follow-up, 4·4% of primary care patients met ICD-10 criteria for a depressive episode. Logistic regression analysis revealed that the recognition by the general practitioner as a psychiatric case, repeated suicidal thoughts, previous depressive episodes, the number of chronic organic diseases, poor general health, and a full or subthreshold ICD-10 disorder were related to the development of new depressive episodes.Conclusions. Psychological/psychiatric problems were found to play the most important role in the prediction of depressive episodes while sociodemographic variables were of lower importance. Differences compared with other studies might be due to our prospective design and possibly also to our culturally different sample. Applied stratification procedures, which resulted in a sample at high risk of developing depression, might be a limitation of our study.

Risk Factors for the Onset of Bipolar Disorder and Factors Influencing Recognition: Results from NEMESIS

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
E.J. Regeer ◽  
J. Spijker
Keyword(s):  
Risk Factors ◽  
Bipolar Disorder ◽  
Predictive Value ◽  
Cannabis Use ◽  
Adequate Treatment ◽  
Predictive Values ◽  
Manic Symptoms ◽  
Factors Influencing ◽  
Subclinical Depression ◽  
Subclinical Psychosis

Aims:Risk factors for the onset of bipolar disorder and factors influencing recognition were examined in a general population sample.Method:In the Netherlands Mental Health Survey and Incidence Study (NEMESIS) symptoms of depression, mania, psychosis and substance use were assessed among 7076 respondents with the Composite International Diagnostic Interview at baseline, after one and after three years. In a reappraisal study among 40 respondents with bipolar disorder confirmed by the Structured Clinical Interview for DSM-IV (SCID) data on illness and treatment history were collected.Results:Predictive values of subclinical depression and (hypo)mania for bipolar disorder ranged from 14.3% to 50%. Cannabis use at baseline increased the risk for manic symptoms during follow-up (OR 2.70, 95%CI:1.54-4.75) (Henquet et al., 2006). Comorbid subclinical psychosis in respondents with subclinical mania had predictive value for future diagnosis of bipolar disorder (positive predictive value of 3% versus 10% respectively) (Kaymaz et al., 2007). The majority of the respondents with a SCID diagnosis bipolar disorder consulted a health professional, only 12.5% received a diagnosis of bipolar disorder and agreed with the diagnosis. Only these respondents used a moodstabilizer and had contact with a psychiatrist. Type of bipolar disorder, number of mood episodes and age of onset did not influence recognition.Conclusion:Subclinical depression and (hypo)mania, and comorbity of subclinical psychosis and mania are predictive for future diagnosis of bipolar disorder. Cannabis use affects the expression of manic symptoms. Self-recognition of bipolar disorder is an important factor in treatment seeking and receiving adequate treatment.

scholarly journals Development of data-driven framework for automatically identifying patient cohorts from linked electronic health records

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Fabiola Fernández-Gutiérrez ◽  
Jonathan Kennedy ◽  
Roxanne Cooksey ◽  
Mark Atkinson ◽  
Ernest Choy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Primary Care ◽  
Secondary Care ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Identification Algorithm ◽  
Learning Context ◽  
Predictive Values ◽  
Selection Of

ABSTRACTObjectives 1) To develop a fully data-driven framework for automatically identifying patients with a condition from routine electronic primary care records; 2) to identify informative codes (risk factors) of arthropathy conditions in primary care records that can accurately predict a diagnosis of the conditions in secondary care records. ApproachThis study linked routine primary and secondary care records in Wales, UK held in the SAIL (Secured Anonymised Information Linkage) databank, in which the secondary care records were used as golden standard. As such, we proposed to use machine learning techniques to extract patient information and identify cohorts with a condition from the large and high-dimensional linked dataset using the following phases: data preparation, performed in the machine learning context fashion; pre-selection of initial features, ranking and selecting features into a meaningful subset by using feature selection methods; and identification algorithm development which incorporates mechanisms of tackling the imbalanced nature of the data. This data-driven framework was then validated on an independent dataset, and compared with existing algorithm which had been developed using expert clinician knowledge for arthropathy conditions. ResultsRheumatoid arthritis (RA) and ankylosing spondylitis (AS) were used to demonstrate the feasibility of this framework. Linking primary care records with the secondary care rheumatology clinical system, we collected 9,657 patients with 1,484 RA patients and 204 AS patients. The proposed framework identified various compact subsets of informative features (risk factors) from 43,100 potential Read codes. Applying to an independent test data, this framework achieved the classification accuracy and positive predictive values (PPVs) of 86.19% and 88.46% respectively for RA and 99.23 % and 97.75% respectively for AS, which are comparable with the performance of clinical knowledge-based method - the accuracy of 85.85%, the PPV of 85.28% for RA and the accuracy of 97.86% , the PPV of 95.65% for AS. ConclusionThe proposed data-driven framework provides a rapid and cost-effective way of reliably identifying patients with a medical condition from primary care data. It performed as well as the clinically derived algorithm. This framework does not intend to substitute clinical expertise, instead it provides an decision support tool for clinicians during their decision process, in particular selection of patients for clinical trials.

Predictors for oral and general health outcomes and quality of life among older people attending general practice (GP) clinics in South Australia

2020 ◽  
Author(s):  
Madhan Balasubramanian ◽  
Dominic Keuskamp ◽  
Najith Amarasena ◽  
David Brennan
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Primary Care ◽  
Older People ◽  
Health Outcomes ◽  
General Health ◽  
Chronic Conditions ◽  
Living Arrangement ◽  
South Australia

Abstract Background: As the proportion and number of older people in Australia continue to grow, innovative means to tackle primary care and prevention are necessary to combat the individual, social and economic challenges of non-communicable diseases.Objective: To assess risk factors (or predictors) for oral and general health outcomes and quality of life of older people (75+ yrs.) attending general practice (GP) clinics in South Australia.Methods: Data were collected from older people attending 48 GP clinics in metropolitan South Australia. Age, sex, education, living arrangement, material standards, chronic conditions and nutrition were assessed as risk factors. Global self-rated oral and general health and quality of life (OHIP Severity and EQ-5D Utility) were included as outcome measures.Results: A total of 459 participants completed the study; response rate was 78%. In the adjusted models, high satisfaction with material standards and good nutritional health were positively associated with all four oral and general health measures. Sex (β=-0.07), age (β=-0.09) and number of chronic conditions (β=-0.13) were negatively associated with EQ-5D, while living arrangement (β=0.06) was positively associated. Further, education level (PR:0.78), living arrangement (PR:0.75) and chronic conditions (PR:1.54) were significantly associated with self-rated general health.Conclusion: Satisfaction with material standards and nutritional risk were consistent predictors for oral and general health outcomes and quality of life of older people visiting GP clinics. Primary care teams involving general practitioners, nurses and allied health practitioners are well poised to assess risk factors for older people, and work alongside the dental team.

scholarly journals Implementing screening for social determinants of health using the Core 5 screening tool

2021 ◽  
Vol 10 (3) ◽  
pp. e001362
Author(s):  
Alison Bradywood ◽  
Treasa "Susie" Leming-Lee ◽  
Richard Watters ◽  
Craig Blackmore
Keyword(s):  
Risk Factors ◽  
Health Outcomes ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Screening Tool ◽  
Social Needs ◽  
Social Risk ◽  
Social Risk Factors ◽  
Risk Screening ◽  
The Core

Social determinants of health (SDOH) have been documented to underpin 80% of overall health and are being increasingly recognised as key factors in addressing tertiary health outcomes. Yet, despite the widespread acceptance of the association of SDOH with health outcomes, more than two-thirds of hospitals do not screen for social risk factors that indicate individual-level adverse SDOH. Such screening for social risk factors represents the first step in connecting patients with resources and documents the prevalence of social needs. The aim of this project was to implement the Core 5 social risk screening tool and evaluate its efficacy and usability in identifying social risk factors in a presurgical spine population. Prior to this implementation, screening for social risk had not been performed. The Model for Improvement provided a framework for implementing and evaluating the Core 5 social risk screening tool. Methods included implementation of a patient self-report social risk screening tool, referral workflow to connect patients with needed resources and evaluation of staff feasibility in using the Core 5 tool. The results indicated that the screening tool identified patients with social risk factors and staff reported perceptions of efficacy and usability in clinical workflow. Overall, 52 of 88 (59%) of subjects in the presurgical spine population were effectively screened. Of these, five patients (10%) had identified social needs that needed to be addressed prior to surgery. The staff usability survey for the Core 5 tool demonstrated high acceptance and usability, with an average score of 4.4 (out of 5). Future work should evaluate the efficacy of the screening tool in other ambulatory and tertiary settings.

PREP — The Preoperative Risk Education Package: an online educational resource for primary care clinicians to encourage community based prehabilitation prior to major surgery

2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X697277
Author(s):  
James Durrand ◽  
F McHardy ◽  
E Land ◽  
Z Llewellyn ◽  
C Norman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Perioperative Risk ◽  
Perioperative Outcomes ◽  
Major Surgery ◽  
Educational Resource ◽  
Major Step ◽  
Early Evaluation ◽  
Risk Education ◽  
Primary Care Clinicians

BackgroundPrehabilitation prior to major surgery mandates cross-sector working. Utilising the preoperative window from referral requires clinician engagement. Awareness of perioperative risk factors is crucial. A national survey uncovered gaps in knowledge and understanding.AimCreate an open-access, online educational resource for primary care clinicians.MethodOur multidisciplinary team developed a focused CPD resource targeting lifestyle factors and chronic health conditions influencing perioperative risk (www.prepwell.co.uk).ResultsPREP highlights seven risk factors influencing perioperative risk: Smoking, alcohol, inactivity, anaemia, cognitive impairment, frailty and low BMI. A case study frames each factor alongside perioperative impact and prehabilitation strategies.ConclusionPREP is the first educational resource of its type. Early evaluation through local clinicians, the RCGP and RCOA has resulted in very positive feedback. We are working with Royal College representatives to gain formal endorsement and facilitate wider scale rollout, a major step towards raised clinician awareness and enhanced collaboration for improved perioperative outcomes.

Sign in / Sign up

Export Citation Format

Share Document